Research on defect detection of bottle cap interior based on low-angle and large divergence angle vision system.

During the machine vision inspection of the inner section of bottle caps within pharmaceutical packaging, the unique conca bottom and convex side walls often create obstructions to the illumination. Consequently, this results in challenges such as irregular background and diminished feature contrast in the image, ultimately leading to the misidentification of defects. As a solution, a vision system characterized by a Low-Angle and Large Divergence Angle (LALDA) is presented in this paper. Using the large divergence angle of LED, combined with low-angle illumination, a uniform image of the side wall region with bright-field characteristics and a uniform image of inner circle region at the bottom with dark-field characteristics are obtained, thus solving the problems of light being obscured and brightness overexposure of the background. Based on the imaging characteristics of LALDA, a multi-channel segmentation (MCS) algorithm is designed. The HSV color space has been transformed, and the image is automatically segmented into multiple sub-regions by mutual calculation of different channels. Further, image homogenization and enhancement are used to eliminate fluctuations in the background and to enhance the contrast of defects. In addition, a variety of defect extraction methods are designed based on the imaging characteristics of different sub-regions, which can avoid the problem of over-segmentation in detection. In this paper, the LALDA is applied to the defect detection inside the cap of capsule medicine bottle, the detection speed is better than 400 pcs/min and the detection accuracy is better than 95%, which can meet the actual production line capacity and detection requirements.

Machine vision model for detection of foreign substances at the bottom of empty large volume parenteral.

Empty large volume parenteral (LVP) bottle has irregular shape and narrow opening, and its detection accuracy of the foreign substances at the bottom is higher than that of ordinary packaging bottles. The current traditional detection method for the bottom of LVP bottles is to directly use manual visual inspection, which involves high labor intensity and is prone to visual fatigue and quality fluctuations, resulting in limited applicability for the detection of the bottom of LVP bottles. A geometric constraint-based detection model (GCBDM) has been proposed, which combines the imaging model and the shape characteristics of the bottle to construct a constraint model of the imaging parameters, according to the detection accuracy and the field of view. Then, the imaging model is designed and optimized for the detection. Further, the generalized GCBDM has been adopted to different bottle bottom detection scenarios, such as cough syrup and capsule medicine bottles by changing the target parameters of the model. The GCBDM, on the one hand, can avoid the information at the bottom being blocked by the narrow opening in the imaging optical path. On the other hand, by calculating the maximum position deviation between the center of visual inspection and the center of the bottom, it can provide the basis for the accuracy design of the transmission mechanism in the inspection, thus further ensuring the stability of the detection.

Exploring Discriminative Word-Level Domain Contexts for Multi-Domain Neural Machine Translation.

Owing to its practical significance, multi-domain Neural Machine Translation (NMT) has attracted much attention recently. Recent studies mainly focus on constructing a unified NMT model with mixed-domain training corpora to switch translation between different domains. In these models, the words in the same sentence are not well distinguished, while intuitively, they are related to the sentence domain to varying degrees and thus should exert different effects on the multi-domain NMT model. In this article, we are committed to distinguishing and exploiting different word-level domain contexts for multi-domain NMT. For this purpose, we adopt multi-task learning to jointly model NMT and monolingual attention-based domain classification tasks, improving the NMT model in two ways: 1) One domain classifier and one adversarial domain classifier are introduced to conduct domain classifications of input sentences. During this process, two generated gating vectors are used to produce domain-specific and domain-shared annotations for decoder; 2) We equip decoder with an attentional domain classifier. Then, the derived attentional weights are utilized to refine the model training via word-level cost weighting, so that the impacts of target words can be discriminated by their relevance to sentence domain. Experimental results on several multi-domain translations demonstrate the effectiveness of our model.